import numpy as np
import scipy.io as sio
import os

def main():
    data = sio.loadmat('D:/data/save/AEEEM/' + 'EQ' + '.mat')['EQ']

    dirList = ['AEEEM', 'MORPH', 'NASA', 'Relink', 'SOFTLAB']

    for dir in dirList:
        for filename in os.listdir(f'./dataSetMat/{dir}'):
            # print(filename.split('.')[0])
            fn = filename.split('.')[0]
            l = '{'
            r = '}'
            txt = f'''
            \"{fn}\":{l}
                "labelRatio": 0.99,
                "Gamma": 2,
                "Mu": 0.9,
                "xMaxScaler": 0.0001,
                "yScaler": 100000,
                "threshold": 6
            {r},
            '''
            print(txt)

if __name__ == '__main__':
    main()

    # "XGBOD": XGBOD(random_state=0),

    # "BaggingClassifierPU": BaggingClassifierPU(
    #     DecisionTreeClassifier(),
    #     n_estimators=1000,  # 1000 trees as usual
    #     max_samples=sum(y_train),  # Balance the positives and unlabeled in each bag
    #     n_jobs=-1  # Use all cores
    # )